Improving the Team-work in Heterogeneous Multi-agent Systems: Situation Matching Approach

  • Authors:

  • Salvador Ibarra;Christian Quintero;Didac Busquets;Josep Ramón;Josep Ll. de la Rosa;José A. Castán

  • Affiliations:

  • Agents Research Lab, University of Girona, Spain;Agents Research Lab, University of Girona, Spain;Agents Research Lab, University of Girona, Spain;Agents Research Lab, University of Girona, Spain;Agents Research Lab, University of Girona, Spain;Facultad de Ingeniería, Universidad Autónoma de Tamaulipas, México

  • Venue:
  • Proceedings of the 2006 conference on Artificial Intelligence Research and Development
  • Year:
  • 2006

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Abstract

This paper presents a method called “Situation Matching” that aids to improve cooperative tasks in heterogeneous multi-agent systems. The situation matching (SM) above represent a match between the system requirements and the agents' capabilities. In this sense, the agents have a set of information denoted “Agent Situation” by means of three parameters: Proximity, Introspection and Trust. Thus, the agents are represented by autonomous mobile cooperative robots. In our approach, the agents have different controllers to generate dynamic diversity (heterogeneity) in the system. So, these systems can be considered as a team of heterogeneous agents that must work together to fulfill some cooperative tasks. In particular, this paper studies how the heterogeneous agents' performance improves by means of the “situation matching”. Conclusions show the advantages of our proposal in the improvement of intelligent agents' performance in the robot soccer testbed.